Curated Video
Data Science 🐍 Interpolation
Interpolation constructs new prediction points from a discrete set of known data points. There are many types of interpolation such as nearest neighbor (piecewise constant), linear, polynomial, cubic spline, and basis spline. In...
Curated Video
Data Science 🐍 Prepare Data
Much of data science and machine learning work is getting clean data into the correct form. This may include data cleansing to remove outliers or bad information, scaling for machine learning algorithms, splitting into train and test...
APMonitor
Data Science 🐍 Graphical Analysis
In addition to summary statistics, data visualization helps to understand the data characteristics and how different variables are related.There are many examples of data visualization with Matplotlib, Seaborn, and Plotly. In this...
Curated Video
Data Science 🐍 Regression
Regression is the process of adjusting model parameters to fit a prediction to measured values. There are independent variables as inputs to the model to generate the predictions. For machine learning, the objective is to minimize a loss...
Curated Video
Data Science 🐍 Import / Export
Python has functions for reading, creating, and deleting files. The high-level steps for many data science applications is to import data, analyze data, and export results. A basic function for working with files is open(filename,mode)....
Curated Video
Data Science 🐍 Features
Features are input values to regression or classification models. The features are inputs and labels are the measured outcomes. Classification predicts discrete labels (outcomes) such as yes/no, True/False, or any number of discrete...
Curated Video
Data Science 🐍 Statistical Analysis
Once data is read into Python, a first step is to analyze the data with summary statistics. This is especially true if the data set is large. Summary statistics include the count, mean, standard deviation, maximum, minimum, and quartile...
Curated Video
Data Science 🐍 Classification
Classification predicts discrete labels (outcomes) such as yes/no, True/False, or any number of discrete levels such as a letter from text recognition, or a word from speech recognition. There are two main methods for training...
Curated Video
Data Science 🐍 Install and Overview
Welcome to the course on data science with Python. This course steps through basic data science and machine learning skills to analyze data and create actionable information. It address major steps of the Cross-Industry Standard Process...
Curated Video
Measures of Central Tendency and Grouped Data: Examples and Estimations
This video is a lecture on measures of central tendency, specifically on how to find the mean for grouped data using coding sets. The lecturer explains the importance of summarizing large data sets and gives examples of various measures...
Curated Video
Data Science 🐍 Python Course
Python 🐍 Data Science with the TCLab Welcome to this data science course on Python! This course is intended to help you develop data science and machine learning skills in Python. As with the beginning course, this course has video...
APMonitor
Data Science 🐍 Time Series
Time series data is produced sequentially as new measurements are recorded. Models derived from the data give insight into what happens next. They also show how the system can be changed to achieved a different future outcome. Time...
Curated Video
Data Science 🐍 Differential Equations
Specific types of equations with differential terms arise from fundamental relationships such as conservation of mass, energy, and momentum. Dynamic models can either be regressed (identified) from data or developed without data with...
Curated Video
Data Science 🐍 Solve Equations
Equations are at the root of data science. It is what turns data into actionable information by developing mathematical expressions that mimic physical systems. There are two primary ways to solve equations. The first method is a numeric...
Packt
High Performance Scientific Computing with C 1.3: Interpolation and Extrapolation
How can we "fill in" the data points between discrete data? How can we extend beyond our data points? • Learn linear interpolation • Learn polynomial interpolation • See the dangers of extrapolation
Packt
Python 3: Project-based Python, Algorithms, Data Structures - Inheritance, subclasses and complete example class
A look at how to format print statements and use special characters within strings This clip is from the chapter "Python in-depth" of the series "Python 3: Project-based Python, Algorithms, Data Structures".This section is about Python...
Curated Video
Understanding Scatter Graphs: Identifying Correlations and Making Predictions
The video discusses scatter graphs and their usefulness in determining the relationship between two sets of data. The speaker explains the concepts of positive, negative, and no correlation, and how to identify them on a scatter graph....
Curated Video
Linear and Nonlinear Regression in Python
Polynomial or general nonlinear functions are developed with Numpy and Scipy in Python. These exercises also cover methods to create linear or spline interpolations that interpolate between data points.
Curated Video
Statistical Regression Models and Predicting Values
This video discusses how to determine the best statistical regression model to approximate data within a scatter plot and make predictions through interpolation and extrapolation. It covers the process of inputting data into a graphing...
Curated Video
How to Draw a Scatter Diagram and Make Conclusions Based on Correlation
The video teaches how to draw a scatter diagram and make conclusions based on the correlation from that corresponding scatter diagram. Exploratory and response variables are introduced and various types of correlations are explained,...
MinuteEarth
Proteins: Explained
To start using Tab for a Cause, go to: http://tabforacause.org/minuteearth2 You might already know that proteins are a fundamental part of your diet, but they're much more than that. LEARN MORE ************** To learn more about this...
Packt
High Performance Scientific Computing with C 1.2: Introduction – Why Use Computers for Math?
Why is the history of computation so tied with mathematics? How are computers used today to solve mathematical problems? • Understand the need for computers to solve mathematical problems • Understand the problems for which computers are...
Packt
High Performance Scientific Computing with C 1.4: Numerical Integration
How can we calculate integrals with a computer? How can we solve differential equations? • Calculate integrals with the trapezoid and Simpson’s rule • See how the error terms scale with different algorithms • Solve differential equations...
Curated Video
The Limits of Correlation: Understanding Causation and Making Predictions
The video is a lecture on the limits of correlation in statistics. The speaker discusses the importance of being careful when interpreting correlations and emphasizes that correlation does not infer causation. The video covers topics...